Product brand naming

Skill whjstc/agent-skills/product-brand-naming

Product, startup, SaaS, AI app, API, devtool, and digital-product naming workflow for market-informed brand naming. Use this whenever the user asks to name or rename a product or company, choose a domain, validate brand-name candidates, compare product names, check domain availability, research competitor naming patterns, do obvious same-name search, or perform preliminary trademark/conflict screening before launch.From its SKILL.md

Install

npx -y skills add whjstc/agent-skills --skill product-brand-naming

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SKILL.md

7.1 KB, ~1.5k tokens by cl100k_base, as published. Nobody here has run it

Product Brand Naming

Overview

Use this skill to create commercially usable brand-name shortlists for SaaS products, AI apps, API products, devtools, marketplaces, content products, and other digital businesses. The workflow is repeatable: scan the market, clarify positioning, ask the user to choose a naming direction, generate candidates, screen obvious conflicts, check domains, flag trademark risk, and return a ranked decision table.

This skill is for product and brand planning, not legal advice. Trademark findings are preliminary risk signals only; recommend counsel review before public launch, Stripe setup, OAuth app verification, legal pages, or paid acquisition.

Workflow

0. Scan the market landscape

Read references/market-scan.md before generating names when the task involves a real product category, launch decision, or domain purchase.

If browsing is available, search current competitors and adjacent products first. If browsing is unavailable, use known competitors plus any user-provided examples and clearly label the scan as limited.

Capture:

Competitors and adjacent products
Their names, domains, taglines, and categories
Naming patterns used in the category
Overcrowded words and weakly differentiated patterns
Risk words that imply certification, legal certainty, regulated status, or guarantees
Naming whitespace: credible directions that are still underused

Use the scan to shape candidate generation. Do not copy competitor names, imitate distinctive coined names too closely, or recommend a name only because the domain appears available.

1. Build the naming brief

Extract the brief from the conversation first. If important fields are missing, ask only the highest-impact question.

Capture:

Product category and ICP
Product shape: SaaS, AI app, API, devtool, plugin, marketplace, service, open-source project, etc.
Primary job-to-be-done
Desired tone: technical, trustworthy, premium, friendly, sharp, enterprise, etc.
Words to include or avoid
Legal/claims constraints, especially words that imply certification, compliance, guarantees, or regulated status
Preferred TLDs and launch markets
Competitors and adjacent categories

2. Ask for a naming direction

After the market scan and brief, present 4-6 naming directions and ask the user to choose before generating the full candidate set.

Use a compact table:

Option	Direction	Best For	Tradeoff	Example Shape

Common directions:

Clear/descriptive: low explanation cost, stronger SEO/category signal
Technical/audit: credible for technical buyers, may feel colder
Trust/privacy-led: reassuring for business buyers, may blend into privacy-platform language
Detection/visibility: strong for monitoring, evidence, and diagnostics products
Coined/brandable: more ownable and easier to trademark, higher explanation cost
Metaphorical: memorable and flexible, needs stronger tagline support

Question format:

Which naming direction should I prioritize? Pick one or two. If you want speed, I can choose the best mix based on the market scan.

If the user has explicitly asked to proceed without questions or says "you decide", select 2-3 directions yourself and state the selection before generating names. Otherwise, stop and wait for the user's answer.

When the user chooses, allocate roughly 70% of generated candidates to the selected direction(s) and 30% to adjacent exploratory directions so the shortlist does not become too narrow.

3. Generate candidates in lanes

Generate 25-60 candidates before screening. Use several lanes so the list does not collapse into one naming pattern:

Descriptive: clear category signal, e.g. ConsentProbe
Evocative: implies outcome or operating model, e.g. SignalVault
Compound: two familiar words, e.g. TagRadar
Invented/lightly coined: brandable but still pronounceable, e.g. Consento
Future-proof: works if the product grows beyond the initial feature

Avoid names that:

Overpromise legal certainty: Compliant, Certified, Proof, Legal
Are too narrow for the roadmap unless deliberately scoped: Pixel when the product audits cookies, storage, requests, CMPs, and GPC too
Are hard to say, spell, or hear over a call
Depend on punctuation, unusual capitalization, or exact stylization to make sense

4. Screen domains

Use scripts/check-domains.mjs for the first pass:

node /path/to/product-brand-naming/scripts/check-domains.mjs ConsentProbe ConsentRadar --tlds=com,io,app

Interpret results conservatively:

likely_available: no registration found via RDAP/WHOIS signal; verify at a registrar before recommending purchase
registered: likely unavailable or requires acquisition
unknown: do not treat as available; re-check manually or with a registrar

Prefer .com for the primary brand if feasible. Treat .io, .app, or category TLDs as acceptable fallbacks only if the user understands the tradeoff.

5. Screen obvious conflicts

For finalists, run exact-match searches and adjacent-category searches:

"Name"
"Name" SaaS
"Name" software
"Name" app
"Name" trademark
"Name" + category keyword
Domain string, e.g. "name.com"

If available and relevant, check USPTO, WIPO Global Brand Database, EUIPO, Companies House, GitHub, npm, X, LinkedIn, Product Hunt, and app marketplaces.

6. Score and shortlist

Read references/scorecard.md when producing a ranked table or making a recommendation.

Score each finalist on:

Category clarity
Brand distinctiveness
Memorability and pronunciation
Domain strength
Search uniqueness
Trademark/conflict risk
Roadmap fit
Claim-safety

7. Output format

Return:

Naming brief summary
Market naming scan summary
Naming pattern takeaways and whitespace
User-selected naming direction or agent-selected direction with rationale
Top 5-10 ranked candidates
Domain status table
Conflict/trademark-risk notes
Recommendation and backup option
Next actions before adopting the name

Use direct risk language:

low obvious conflict found means only no obvious issue found in quick screening
needs manual verification means domain/search data is incomplete
higher legal/name risk means do not use without deeper review

Optional External Skills

If these skills are installed, use them as supporting references; do not require them:

product-name for product naming framing
brand-naming-strategies for naming lanes
domain-name-brainstormer for domain variants
search-domain-validator for availability checks
trademark-search or uspto-database for trademark screening

If they are not installed, proceed with this skill's workflow and available web/RDAP/WHOIS tools.

What ships with it: 4 files

9.9 KB alongside SKILL.md, 1 of them executable

Gives 0 of the 12 instructions most product growth skills give in ~1.5k tokens

Counted across 728 of the 1,010 authors here whose files we hold, read 2026-08-07

Read product marketing context before asking questionsin 24 of 728, across 18 files
Define the ideal customer profilein 21 of 728, across 3 files
Document a rollback plan before deploymentin 21 of 728, across 12 files
Analyze the codebase to understand the productin 19 of 728, across 1 file
Ask clarifying questions about the value propositionin 19 of 728, across 1 file
Search for companies matching the criteriain 19 of 728, across 1 file
Look for signals of immediate needin 19 of 728, across 1 file
Assign a fit score from one to tenin 19 of 728, across 1 file
Identify the target decision-maker rolein 19 of 728, across 1 file
Suggest a personalized contact strategyin 19 of 728, across 1 file
Provide conversation starters for outreachin 19 of 728, across 1 file
Format results in a scannable markdown templatein 19 of 728, across 1 file

Said here and by no other author read

read the market scan reference before naming
search current competitors and adjacent products first
extract the naming brief from the conversation
ask the user to choose a naming direction
generate 25 to 60 candidates before screening
run the domain checking script for candidates

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.